Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics in This Category

Remote healthcare R&D documents cover various stages. These include preclinical research reports, trial protocols, data analysis results, and regulatory submission materials. Data sources are extensive. They include electronic health record systems, medical device logs, genetic sequencing reports, and research papers. Documents update frequently, especially during clinical trials, with real-time or daily updates. Document structures vary. They include highly structured tabular data and large amounts of unstructured text. Examples of unstructured text are physician diagnostic records, patient feedback, and researcher experiment notes. Fields and units are highly specialized. Examples include dosage (mg/kg), biomarker concentration (ng/mL), imaging metrics (mm³), disease diagnostic codes (ICD-10), and drug codes (ATC).

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The high update frequency of remote healthcare R&D documents requires multi-turn conversation systems to quickly index and update the knowledge base. This ensures query results are timely. Diverse document structures, especially large amounts of unstructured text, make precise information extraction and context understanding challenging. Prompt design must focus more on semantic understanding and entity recognition. Highly specialized fields and units require the model to accurately parse and perform unit conversions or associations. This avoids errors due to misunderstanding professional terminology. For example, when a user asks about "the effect of a specific drug on a certain biomarker," the system must extract dosage, biomarker changes, and their statistical significance from complex trial reports. In multi-turn conversations, users may progressively refine query conditions. The system must maintain conversational coherence and gradually focus.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`2048 characters`	Accommodates longer descriptions common in remote healthcare R&D documents, such as research backgrounds and methodologies, ensuring context completeness.
`Chunk Length`	`500 characters`	Balances text semantic integrity with retrieval efficiency, preventing long paragraphs from diluting key information.
`Recall Count`	`Top 8`	Covers more potentially relevant document snippets, especially when dealing with complex queries and multi-dimensional information.
`Similarity Threshold`	`0.75`	Ensures the professionalism and accuracy of recalled content, reducing interference from irrelevant or low-quality information.
`Rerank Return Count`	`Top 3`	Prioritizes displaying core information most directly relevant to the user's query, improving answer focus.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Supports uploading large clinical trial reports and imaging analysis documents.

Three Common Mistakes

A 404 status code appears in the conversation with no response body. This usually results from an incorrect backend service interface address configuration or a network proxy issue. Check model-related environment variables like OPENAI_API_BASE.
Refreshing the page shows "No available index model detected." This often means the knowledge base and model binding relationship is not correctly saved, or the index rebuilding task failed. Check the knowledge base's index status and model binding configuration.
The original document link is not returned after document parsing. This indicates the ENABLE_FILE_SOURCE_RETURN parameter is not enabled, or file storage service (e.g., S3) access permissions are not correctly configured.

How to Confirm Correct Configuration

Perform multi-turn conversation tests. Verify the system accurately understands and responds to complex queries containing specialized terms and units. Pay particular attention to key information like dosages and indicator changes.
Simulate uploading different types of R&D documents (e.g., clinical trial reports, genetic sequencing results). Check if the parsed knowledge snippets are complete and semantically accurate. Also, verify if key fields are correctly extracted.
Check if the conversation results correctly return the source link of the original document. Ensure the link can be clicked to jump to the corresponding paragraph in the original text, verifying the source traceability function.
Monitor system logs. Confirm no timeout errors or rate limit restrictions occur during high-concurrency queries. This ensures system stability.

Note: The values provided are common starting points. Measure them against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.